Litter quality outweighs climate as a driver of decomposition across the tundra biome
Bibliographic record
Abstract
Considerable uncertainty exists regarding the strength, direction and relative importance of the drivers of decomposition in the tundra biome, partly due to a lack of coordinated decomposition field studies in this remote environment. Here, we analysed 3717 incubations of two uniform litter types, green and rooibos tea, buried at 330 circum-Arctic and alpine sites to quantify the effects of temperature, moisture and litter quality on decomposition. We found a surprisingly linear positive relationship between decomposition and soil temperature across all sites, counter to theory and previous model estimates. Litter mass loss was greater at wetter sites, even where soils reached almost full water saturation. However, litter quality was the strongest driver of litter mass loss across the tundra biome, explaining six times more variation in summer decomposition than soil temperature. Our results indicate that climate warming will directly increase decomposition across tundra environments. However, the indirect effects of climate change on vegetation communities, and thus plant litter inputs and quality, could have a more profound impact than direct effects on the balance of this globally important carbon store.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".